Exploratory Data Analysis for the Nigerian Property Market

Author: Temitope Obasa

Introduction

This Exploratory Data Analysis project was carried out to gain insights on the Nigerian Property Market.

The Federal Republic of Nigeria, is a country in West Africa and the most populous country in Africa with a population of over 216 million. Nigeria borders Niger in the north, Chad in the northeast, Cameroon in the east, and Benin in the west. Nigeria consists of 36 Federal states and the Federal Capital Territory, Abuja. Lagos is the largest city in Nigeria and the second-largest in Africa.

The dataset used was obtained from Kaggle (https://www.kaggle.com/datasets/abdullahiyunus/nigeria-houses-and-prices-dataset). It contains data on Lagos Property prices scrapped by Abdullahi Yunus ( https://www.kaggle.com/abdullahiyunus).

Business Problem

In order gain a clear understanding of the Nigerian Real Estate market, an Exploratory Data Analysis project is necessary. This EDA project focuses on;

- property prices in Nigeria.

- property types in the Nigerian Real Estate Market

- common features of properties in the Nigerian Real Estate Market.

- factors that influence property prices in the Nigerian Real Estate Market.

Categorical Features

Bedrooms - Number of bedrooms

Toilets - Number of toilets

Bathrooms - Number of bathrooms

State - Federal State

Title - Property type

Town - Town in a federal state

Importing Libraries

# Import essential libraries for Data Analysis

import pandas as pd
import numpy as np
import statistics as st

# Import essential library for splitting datetime
import datetime as dt

# Importing essential library for downloading the dataset from Kaggle
# import opendatasets as od

# Import essential libraries for Exploratory Data Analysis and Visualization
import matplotlib as ploty
import matplotlib.pyplot as plt
import seaborn as sns
from statistics import mode
from scipy.stats import iqr
import plotly.express as px

# Ignore Warnings

import warnings
warnings.filterwarnings("ignore")

Exploring the Dataset

Loading the Dataset

# This loads the dataset and prints the first five rows in the dataframe
df = pd.read_csv("/Users/temi/Datasets/nigeria_houses_data.csv")
df.head(5)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	6	5	5	4	Detached Duplex	Mabushi	Abuja	450000000.0
1	4	5	5	4	Terraced Duplexes	Katampe	Abuja	800000000.0
2	4	5	5	4	Detached Duplex	Lekki	Lagos	120000000.0
3	4	4	5	6	Detached Duplex	Ajah	Lagos	40000000.0
4	4	4	5	2	Semi Detached Duplex	Lekki	Lagos	75000000.0

df.tail()

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
24321	2	2	2	4	Block of Flats	Kabusa	Abuja	15000000.0
24322	4	5	5	4	Block of Flats	Ado-Odo/Ota	Ogun	25000000.0
24323	4	5	5	4	Detached Duplex	Lekki	Lagos	68000000.0
24324	3	4	4	3	Block of Flats	Victoria Island (VI)	Lagos	78000000.0
24325	4	5	5	4	Detached Duplex	Lekki	Lagos	65000000.0

df.shape
print(f"It consists of", df.shape[1], "columns and", df.shape[0], "rows.")

It consists of 8 columns and 24326 rows.

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 24326 entries, 0 to 24325
Data columns (total 8 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   bedrooms       24326 non-null  int64  
 1   bathrooms      24326 non-null  int64  
 2   toilets        24326 non-null  int64  
 3   parking_space  24326 non-null  int64  
 4   title          24326 non-null  object 
 5   town           24326 non-null  object 
 6   state          24326 non-null  object 
 7   price          24326 non-null  float64
dtypes: float64(1), int64(4), object(3)
memory usage: 1.5+ MB

df.describe()

	bedrooms	bathrooms	toilets	parking_space	price
count	24326.000000	24326.000000	24326.000000	24326.000000	2.432600e+04
mean	4.338814	4.600798	5.176355	4.041725	3.013802e+08
std	1.138497	1.163161	1.226253	1.399936	1.220403e+10
min	1.000000	1.000000	1.000000	1.000000	9.000000e+04
25%	4.000000	4.000000	5.000000	4.000000	5.200000e+07
50%	4.000000	5.000000	5.000000	4.000000	8.500000e+07
75%	5.000000	5.000000	6.000000	4.000000	1.600000e+08
max	9.000000	9.000000	9.000000	9.000000	1.800000e+12

Distribution of Features

# Plotting histogram

df.hist(figsize=(16, 16), xrot=90)

plt.show()

Checking Columns

df.columns

Index(['bedrooms', 'bathrooms', 'toilets', 'parking_space', 'title', 'town',
       'state', 'price'],
      dtype='object')

Categorical features

# Summarize the categorical features

df.describe(include=["object"])

	title	town	state
count	24326	24326	24326
unique	7	189	25
top	Detached Duplex	Lekki	Lagos
freq	13992	10895	18445

Federal States in Nigeria

# Identify the states represented in the dataframe

df["state"].unique()

array(['Abuja', 'Lagos', 'Edo', 'Ogun', 'Oyo', 'Imo', 'Anambara',
       'Rivers', 'Enugu', 'Kaduna', 'Kwara', 'Nasarawa', 'Abia', 'Delta',
       'Akwa Ibom', 'Osun', 'Ekiti', 'Cross River', 'Kogi', 'Plateau',
       'Kano', 'Katsina', 'Bayelsa', 'Borno', 'Niger'], dtype=object)

Although there are 36 states (including the national capital) in Nigeria, the dataset only contains data on 25 states.

Towns

# Identify the towns represented in the dataframe

df["town"].unique()

array(['Mabushi', 'Katampe', 'Lekki', 'Ajah', 'Epe', 'Lokogoma District',
       'Oredo', 'Victoria Island (VI)', 'Mowe Ofada', 'Ikeja', 'Ikoyi',
       'Magodo', 'Kaura', 'Galadimawa', 'Gwarinpa', 'Abeokuta North',
       'Lugbe District', 'Ibeju Lekki', 'Yaba', 'Sango Ota',
       'Ifako-Ijaiye', 'Agege', 'Ikorodu', 'Jahi', 'Ibadan', 'Orozo',
       'Ifo', 'Owerri North', 'Guzape District', 'Idu Industrial',
       'Owerri Municipal', 'Isheri North', 'Utako', 'Port Harcourt',
       'Kuje', 'Isheri', 'Life Camp', 'Ipaja', 'Ado-Odo/Ota', 'Dape',
       'Mushin', 'Ejigbo', 'Isolo', 'Ojodu', 'Gaduwa', 'Enugu', 'Dakwo',
       'Asokoro District', 'Alimosho', 'Sagamu', 'Chikun', 'Egbeda',
       'Wuye', 'Kubwa', 'Shomolu', 'Ogudu', 'Owerri West', 'Ibafo',
       'Surulere', 'Obio-Akpor', 'Ayobo', 'Apo', 'Mowe Town',
       'Ibadan South-West', 'Wuse 2', 'Durumi', 'Simawa', 'Arepo',
       'Ikotun', 'Oluyole', 'Maitama District', 'Maryland', 'Ido',
       'Karsana', 'Wuse', 'Ilorin West', 'Kurudu', 'Karmo',
       'Abeokuta South', 'KM 46', 'Gbagada', 'Idimu', 'Kaduna South',
       'Magboro', 'Gudu', 'Kukwaba', 'Mbora (Nbora)', 'Obafemi Owode',
       'Jabi', 'Karu', 'Ojo', 'Garki', 'Aba', 'Ogijo', 'Asaba', 'Uyo',
       'Kosofe', 'Oyigbo', 'Karshi', 'Osogbo', 'Kado', 'Ilupeju',
       'Afijio', 'Ketu', 'Ado-Ekiti', 'Ibadan North', 'Egor', 'Nyanya',
       'Ibarapa North', 'Kyami', 'Ojota', 'Dutse', 'Nasarawa', 'Calabar',
       'Kaduna North', 'Eleme', 'Kafe', 'Oshodi', 'Dakibiyu',
       'Amuwo Odofin', 'Ijede', 'Bwari', 'Dekina', 'Jos South',
       'Agbara-Igbesa', 'Ijaiye', 'Kagini', 'Ohaji/Egbema', 'Mpape',
       'Apapa', 'Lagos Island', 'Gwagwalada', 'Ijoko', 'Diplomatic Zones',
       'Akure', 'Ethiope West', 'Badagry', 'Kano', 'Ede South',
       'Ilorin South', 'Oke-Aro', 'Oke-Odo', 'Ikot Ekpene', 'Kusada',
       'Akinyele', 'Kabusa', 'Dei-Dei', 'Eket', 'Egbe', 'Udu',
       'Ibadan North-East', 'Danja', 'Yewa South', 'Warri', 'Duboyi',
       'Jikwoyi', 'Oyo West', 'Agbara', 'Ovia North-East', 'Yenagoa',
       'Central Business District', 'Guzamala', 'Lokoja', 'Jos North',
       'Orile', 'Wumba', 'Ibadan North-West', 'Eko Atlantic City',
       'Imota', 'Ikwerre', 'Keffi', 'Ilorin East', 'Uvwie', 'Umuahia',
       'Ijesha', 'Okene', 'Aniocha South', 'Ijebu Ode', 'Okpe',
       'Ughelli South', 'Ewekoro', 'Nassarawa', 'Mararaba', 'Uhunmwonde',
       'Ughelli North', 'Ibeju', 'Abraka', 'Paikoro', 'Ikpoba Okha'],
      dtype=object)

Property Types

df["title"].unique()

array(['Detached Duplex', 'Terraced Duplexes', 'Semi Detached Duplex',
       'Detached Bungalow', 'Block of Flats', 'Semi Detached Bungalow',
       'Terraced Bungalow'], dtype=object)

Data Cleaning

Data cleaning involves the process of identifying inaccurate, incomplete or irrelevant parts of the data. This is carried out to prepare the data for analysis or storing in a database. It includes deleting missing values to removing irrelevant rows or columns.

Check for duplicates

df.duplicated().sum()

10438

Remove the duplicates

df.drop_duplicates(keep="first", inplace=True)
df.shape

print(f"The dataframe now consists of", df.shape[1],
      "columns and", df.shape[0], "rows.")

The dataframe now consists of 8 columns and 13888 rows.

By removing the duplicates, 42% of the datapoints are lost. This could be an indication of inaccurate and inefficient data collation techniques.

Checking for missing values

df.isnull().sum()

bedrooms         0
bathrooms        0
toilets          0
parking_space    0
title            0
town             0
state            0
price            0
dtype: int64

There are no missing values in the dataframe.

Outliers

Identifying Outliers

The target column "price" will be checked for potential outliers.

df[["price"]].describe()

	price
count	1.388800e+04
mean	4.002137e+08
std	1.614926e+10
min	9.000000e+04
25%	3.800000e+07
50%	7.500000e+07
75%	1.600000e+08
max	1.800000e+12

Visualise

# Plotting a boxplot to visualise the outliers in price column

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df["price"], y=df["state"],  palette="Set2",)
sns.stripplot(x=df["price"], y=df["state"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='state'>

The box plot above show majority of the dataoiints on the left side, however, there some datapoints on the that appear distant from the other datapoints as you begin to scan towards the right part of the chart. These point indicate the presence of Outliers. These outliers are properties with significantly higher prices when compared to the property prices of other federal states within Nigeria. It shows that "Lagos" and "Abuja" appear to have these outliers.

InterQuantileRange (IQR)

# Calculating q1 and q3 for price
Q1 = df.price.quantile(0.25)
Q3 = df.price.quantile(0.75)
Q1, Q3

(38000000.0, 160000000.0)

# Calculating InterQuantileRange (IQR) for price
IQR = Q3 - Q1

print("The Interquartile Range for price is", IQR)

The Interquartile Range for price is 122000000.0

Lower and Upper Limit

# For price
lower_limit = Q1 - 1.5*IQR
upper_limit = Q3 + 1.5*IQR
lower_limit, upper_limit

(-145000000.0, 343000000.0)

Identifying the Outliers

price_outliers = df[(df.price < lower_limit)
                    | (df.price > upper_limit)]

price_outliers.shape

(1498, 8)

Removing the Outliers

Check the shape before removing the outliers

Nigeria = df.shape
Nigeria

(13888, 8)

Remove the outliers using the IQR

df_outlier_free = df[(df.price > lower_limit)
                     & (df.price < upper_limit)]
df_outlier_free.shape

(12390, 8)

Visualise to validate changes

# Plotting scatter plot for living space

plt.scatter(x=df_outlier_free["price"],
            y=df_outlier_free["price"], color="green",)
plt.show()

# Plotting a boxplot to validate changes

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_outlier_free["price"],
            y=df_outlier_free["state"],  palette="Set2",)
sns.stripplot(x=df_outlier_free["price"],
              y=df_outlier_free["state"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='state'>

There appear to be no outliers left.

Re-plotting the box plot indicates the absence of the outliers initially spotted. However, this chart reveals that "Abuja" and "Lagos" have properties with significantly higher prices than other states within Nigeria. For this reason, the dataset should split into subsets per state and the outliers should be removed per state. This will provide a more accurate analysis.

REAL ESTATE MARKET TREND IN NIGERIA

Distribution of Features of Properties in Nigeria

# Plotting histogram

df_outlier_free.hist(figsize=(16, 16), xrot=90)

plt.show()

Skewness

df_outlier_free.skew(axis=1, skipna=True)

2        2.236068
3        2.236068
4        2.236068
6        2.236068
7        2.236068
           ...   
24315    2.236068
24320    2.236068
24321    2.236068
24322    2.236068
24324    2.236068
Length: 12390, dtype: float64

The price chart shows that the dataset is positively skewed i.e, there are more properties with extreme prices in the dataset. This raises the average property price.

Average property price in Nigeria

average_price = st.mean(df_outlier_free["price"])

print("The average property price in Nigeria is " +
      str(round(average_price, 2)) + " Naira")

The average property price in Nigeria is 88358992.03 Naira

Visualise

# Using Groupby to get mean base rent for each state

average_price = df_outlier_free.groupby("state")["price"].mean()
average_price = average_price.rename("mean")

# Combine using concat
compare_states = pd.concat([average_price], axis=1)

# Plot line charts using Plotly library

fig = px.line(compare_states, title="Mean Property Price per State in Nigeria",
              color_discrete_sequence=["red"])

fig.update_layout(yaxis_title="price", legend_title="state", font_size=12)

fig.update_yaxes(rangemode="tozero")

fig.show()

# Plotting a bar chart

average_price = df_outlier_free.groupby(["state"])["price"]\
                    .mean().sort_index()

fig = px.histogram(x=df_outlier_free["state"].
                   value_counts().sort_index().index,
                   y=average_price,
                   color=df_outlier_free["state"].
                   value_counts().sort_index().index)

fig.update_xaxes(title="state")
fig.update_yaxes(title="Average Property Price per state in Nigeria")
fig.show()

The above chart shows that the following federal states have the highest property prices:

- Abuja

- Anambara

- Bayelsa

- Borno

- Enugu

- Imo

- Lagos

- Rivers

Borno appears to have the highest property prices.

Most Common Property type

most_common = st.mode(df_outlier_free["title"])

print("The most common property type in Nigeria is " +
      str(most_common))

The most common property type in Nigeria is Detached Duplex

Correlation

Correlation (corr()) represents the relationship between columns in a dataframe. The results are measured in a range; -1 to 1.

When the result is 1, it indicates a perfect correlation. This means that the values in these columns increase and decrease simultaneously and are interdependent.

A result of 0.9 indicates a good relationship also. This works as when the result is 1.

A result of -0.9 also indicate a good relationship. However, in this case, when the values in one column rises, the value in the other column reduces.

A result of 0.01 indicates a bad relationship. This means that the values in one column cannot be predicted based on the rise or fall of values in the other column.

This would reveal features that are independent and interdependent on other features.

df_outlier_free.corr()

	bedrooms	bathrooms	toilets	parking_space	price
bedrooms	1.000000	0.699514	0.683452	0.187991	0.433314
bathrooms	0.699514	1.000000	0.805618	0.185427	0.371562
toilets	0.683452	0.805618	1.000000	0.192780	0.396733
parking_space	0.187991	0.185427	0.192780	1.000000	0.108696
price	0.433314	0.371562	0.396733	0.108696	1.000000

# Visualizing the correlations

# Set the size
plt.figure(figsize=(15, 15))

# Plotting a heatmap
sns.heatmap(df_outlier_free.corr()*100, annot=True, fmt='.0f')

<AxesSubplot:>

Usually in real estate, the price is dependent on certian factors such as location, size, furnishing and features of the property. The heat map shows that the "number of bedrooms", "number of toilets", "parking space", and "number of bathrooms" correlates with the property price.

EXPLORING EACH FEDERAL STATE

Although the original dataframe has been cleaned and analysed, a deeper look into the dataframe is necessary. This is because the property prices vary vastly between certain federal states such as Lagos and Ogun. For more accurate analysis, it will be beneficial to explore each state individually.

ABIA STATE

# Selecting data on only Abia

df_abi = df[df["state"] == "Abia"].reset_index(drop=True)
df_abi.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	4	5	5	4	Block of Flats	Aba	Abia	30000000.0
1	4	4	5	6	Detached Duplex	Aba	Abia	40000000.0
2	5	5	5	4	Detached Bungalow	Aba	Abia	26000000.0

df_abi.shape
print(f"The new dataframe has", df_abi.shape[0],
      "rows and", df_abi.shape[1], "columns.")

The new dataframe has 9 rows and 8 columns.

ABUJA

# Selecting data on only Abuja

df_abj = df[df["state"] == "Abuja"].reset_index(drop=True)
df_abj.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	6	5	5	4	Detached Duplex	Mabushi	Abuja	450000000.0
1	4	5	5	4	Terraced Duplexes	Katampe	Abuja	800000000.0
2	3	4	5	4	Terraced Duplexes	Lokogoma District	Abuja	25000000.0

df_abj.shape
print(f"The new dataframe has", df_abj.shape[0],
      "rows and", df_abj.shape[1], "columns.")

The new dataframe has 3118 rows and 8 columns.

AKWA IBOM

# Selecting data on only Akwa Ibom

df_akw = df[df["state"] == "Akwa Ibom"].reset_index(drop=True)
df_akw.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	3	5	5	4	Block of Flats	Uyo	Akwa Ibom	55000000.0
1	2	2	3	5	Detached Bungalow	Uyo	Akwa Ibom	10000000.0
2	5	5	6	4	Terraced Duplexes	Uyo	Akwa Ibom	100000000.0

df_akw.shape
print(f"The new dataframe has", df_akw.shape[0],
      "rows and", df_akw.shape[1], "columns.")

The new dataframe has 25 rows and 8 columns.

ANAMBARA

Describe it

# Selecting data on only Anambara

df_ana = df[df["state"] == "Anambara"].reset_index(drop=True)
df_ana.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	5	5	6	4	Detached Duplex	Lekki	Anambara	260000000.0
1	4	5	5	4	Block of Flats	Ibeju Lekki	Anambara	9000000.0
2	4	4	5	4	Semi Detached Duplex	Lekki	Anambara	75000000.0

df_ana.shape
print(f"The new dataframe has", df_ana.shape[0],
      "rows and", df_ana.shape[1], "columns.")

The new dataframe has 141 rows and 8 columns.

BAYELSA

# Selecting data on only Bayelsa

df_bay = df[df["state"] == "Bayelsa"].reset_index(drop=True)
df_bay.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	4	5	5	4	Block of Flats	Yenagoa	Bayelsa	20000000.0
1	7	8	8	2	Detached Duplex	Yenagoa	Bayelsa	160000000.0

df_bay.shape
print(f"The new dataframe has", df_bay.shape[0],
      "rows and", df_bay.shape[1], "columns.")

The new dataframe has 2 rows and 8 columns.

BORNO

# Selecting data on only Borno

df_bor = df[df["state"] == "Borno"].reset_index(drop=True)
df_bor.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	4	4	5	4	Terraced Duplexes	Guzamala	Borno	105000000.0

df_bor.shape
print(f"The new dataframe has", df_bor.shape[0],
      "rows and", df_bor.shape[1], "columns.")

The new dataframe has 1 rows and 8 columns.

CROSS RIVER

# Selecting data on only Cross River

df_cro = df[df["state"] == "Cross River"].reset_index(drop=True)
df_cro.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	7	5	5	4	Block of Flats	Calabar	Cross River	25000000.0
1	4	4	5	4	Detached Duplex	Calabar	Cross River	25000000.0

df_cro.shape
print(f"The new dataframe has", df_cro.shape[0],
      "rows and", df_cro.shape[1], "columns.")

The new dataframe has 2 rows and 8 columns.

DELTA

# Selecting data on only Delta

df_del = df[df["state"] == "Delta"].reset_index(drop=True)
df_del.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	5	5	6	6	Detached Duplex	Asaba	Delta	100000000.0
1	4	4	5	5	Semi Detached Duplex	Asaba	Delta	65000000.0
2	3	3	4	8	Detached Bungalow	Asaba	Delta	55000000.0

df_del.shape
print(f"The new dataframe has", df_del.shape[0],
      "rows and", df_del.shape[1], "columns.")

The new dataframe has 62 rows and 8 columns.

EDO

# Selecting data on only Edo

df_edo = df[df["state"] == "Edo"].reset_index(drop=True)
df_edo.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	6	6	6	4	Semi Detached Duplex	Oredo	Edo	45000000.0
1	5	5	6	1	Detached Duplex	Oredo	Edo	70000000.0
2	8	8	8	4	Block of Flats	Oredo	Edo	36000000.0

df_edo.shape
print(f"The new dataframe has", df_edo.shape[0],
      "rows and", df_edo.shape[1], "columns.")

The new dataframe has 98 rows and 8 columns.

EKITI

# Selecting data on only Ekiti

df_eki = df[df["state"] == "Ekiti"].reset_index(drop=True)
df_eki.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	2	2	2	4	Block of Flats	Ado-Ekiti	Ekiti	70000000.0
1	4	4	5	4	Detached Bungalow	Ado-Ekiti	Ekiti	70000000.0
2	3	3	4	4	Detached Bungalow	Ado-Ekiti	Ekiti	18000000.0

df_eki.shape
print(f"The new dataframe has", df_eki.shape[0],
      "rows and", df_eki.shape[1], "columns.")

The new dataframe has 9 rows and 8 columns.

ENUGU

# Selecting data on only Enugu

df_enu = df[df["state"] == "Enugu"].reset_index(drop=True)
df_enu.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	4	4	5	4	Detached Bungalow	Enugu	Enugu	28500000.0
1	4	4	5	4	Detached Duplex	Enugu	Enugu	65000000.0
2	6	5	5	4	Detached Bungalow	Enugu	Enugu	20000000.0

df_enu.shape
print(f"The new dataframe has", df_enu.shape[0],
      "rows and", df_enu.shape[1], "columns.")

The new dataframe has 121 rows and 8 columns.

IMO

# Selecting data on only Imo

df_imo = df[df["state"] == "Imo"].reset_index(drop=True)
df_imo.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	5	3	3	4	Detached Duplex	Owerri North	Imo	50000000.0
1	5	5	6	5	Semi Detached Duplex	Owerri Municipal	Imo	60000000.0
2	4	5	5	4	Block of Flats	Owerri Municipal	Imo	130000000.0

df_imo.shape
print(f"The new dataframe has", df_imo.shape[0],
      "rows and", df_imo.shape[1], "columns.")

The new dataframe has 229 rows and 8 columns.

KADUNA

# Selecting data on only Kaduna

df_kad = df[df["state"] == "Kaduna"].reset_index(drop=True)
df_kad.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	5	5	5	1	Terraced Duplexes	Chikun	Kaduna	60000000.0
1	5	5	5	4	Detached Bungalow	Kaduna South	Kaduna	10000000.0
2	7	5	4	3	Detached Duplex	Kaduna South	Kaduna	35000000.0

df_kad.shape
print(f"The new dataframe has", df_kad.shape[0],
      "rows and", df_kad.shape[1], "columns.")

The new dataframe has 23 rows and 8 columns.

KANO

# Selecting data on only Kano

df_kan = df[df["state"] == "Kaduna"].reset_index(drop=True)
df_kan.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	5	5	5	1	Terraced Duplexes	Chikun	Kaduna	60000000.0
1	5	5	5	4	Detached Bungalow	Kaduna South	Kaduna	10000000.0
2	7	5	4	3	Detached Duplex	Kaduna South	Kaduna	35000000.0

df_kan.shape
print(f"The new dataframe has", df_kan.shape[0],
      "rows and", df_kan.shape[1], "columns.")

The new dataframe has 23 rows and 8 columns.

KASTINA

# Selecting data on only Kastina

df_kas = df[df["state"] == "Kastina"].reset_index(drop=True)
df_kas.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price

df_kas.shape
print(f"The new dataframe has", df_kas.shape[0],
      "rows and", df_kas.shape[1], "columns.")

The new dataframe has 0 rows and 8 columns.

KOGI

# Selecting data on only Kogi

df_kog = df[df["state"] == "Kogi"].reset_index(drop=True)
df_kog.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	3	3	3	4	Detached Bungalow	Dekina	Kogi	40000000.0
1	4	4	4	4	Detached Duplex	Lokoja	Kogi	20000000.0
2	3	5	5	1	Block of Flats	Okene	Kogi	40000000.0

df_kog.shape
print(f"The new dataframe has", df_kog.shape[0],
      "rows and", df_kog.shape[1], "columns.")

The new dataframe has 4 rows and 8 columns.

KWARA

# Selecting data on only Kwara

df_kwa = df[df["state"] == "kwara"].reset_index(drop=True)
df_kwa.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price

df_kwa.shape
print(f"The new dataframe has", df_kwa.shape[0],
      "rows and", df_kwa.shape[1], "columns.")

The new dataframe has 0 rows and 8 columns.

LAGOS

# Selecting data on only Lagos

df_lag = df[df["state"] == "Lagos"].reset_index(drop=True)
df_lag.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	4	5	5	4	Detached Duplex	Lekki	Lagos	120000000.0
1	4	4	5	6	Detached Duplex	Ajah	Lagos	40000000.0
2	4	4	5	2	Semi Detached Duplex	Lekki	Lagos	75000000.0

df_lag.shape
print(f"The new dataframe has", df_lag.shape[0],
      "rows and", df_lag.shape[1], "columns.")

The new dataframe has 8571 rows and 8 columns.

NASARAWA

# Selecting data on only Nasarawa

df_nas = df[df["state"] == "Nasarawa"].reset_index(drop=True)
df_nas.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	3	2	2	4	Block of Flats	Karu	Nasarawa	17000000.0
1	3	5	5	4	Detached Bungalow	Nasarawa	Nasarawa	15000000.0
2	3	3	4	3	Detached Bungalow	Karu	Nasarawa	9000000.0

df_nas.shape
print(f"The new dataframe has", df_nas.shape[0],
      "rows and", df_nas.shape[1], "columns.")

The new dataframe has 9 rows and 8 columns.

NIGER

# Selecting data on only Niger

df_nig = df[df["state"] == "Niger"].reset_index(drop=True)
df_nig.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	2	2	3	5	Block of Flats	Paikoro	Niger	40000000.0

df_nig.shape
print(f"The new dataframe has", df_nig.shape[0],
      "rows and", df_nig.shape[1], "columns.")

The new dataframe has 1 rows and 8 columns.

OGUN

# Selecting data on only Ogun

df_ogu = df[df["state"] == "Ogun"].reset_index(drop=True)
df_ogu.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	3	4	4	3	Detached Bungalow	Mowe Ofada	Ogun	12000000.0
1	2	3	5	4	Block of Flats	Mowe Ofada	Ogun	7000000.0
2	2	2	2	4	Detached Bungalow	Abeokuta North	Ogun	4000000.0

df_ogu.shape
print(f"The new dataframe has", df_ogu.shape[0],
      "rows and", df_ogu.shape[1], "columns.")

The new dataframe has 579 rows and 8 columns.

OSUN

# Selecting data on only Osun

df_osu = df[df["state"] == "Osun"].reset_index(drop=True)
df_osu.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	3	4	4	4	Block of Flats	Osogbo	Osun	25000000.0
1	2	3	3	4	Semi Detached Bungalow	Osogbo	Osun	6000000.0
2	1	1	1	2	Detached Bungalow	Osogbo	Osun	135000000.0

df_osu.shape
print(f"The new dataframe has", df_osu.shape[0],
      "rows and", df_osu.shape[1], "columns.")

The new dataframe has 12 rows and 8 columns.

OYO

# Selecting data on only Oyo

df_oyo = df[df["state"] == "Oyo"].reset_index(drop=True)
df_oyo.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	3	3	3	4	Detached Bungalow	Ibadan	Oyo	10000000.0
1	8	1	1	4	Detached Duplex	Ibadan	Oyo	150000000.0
2	3	2	2	4	Detached Bungalow	Ibadan	Oyo	11000000.0

df_oyo.shape
print(f"The new dataframe has", df_oyo.shape[0],
      "rows and", df_oyo.shape[1], "columns.")

The new dataframe has 435 rows and 8 columns.

PLATEAU

# Selecting data on only Plateau

df_pla = df[df["state"] == "Plateau"].reset_index(drop=True)
df_pla.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	2	7	8	7	Detached Duplex	Jos South	Plateau	500000.0
1	7	4	4	4	Detached Duplex	Jos North	Plateau	27000000.0

df_pla.shape
print(f"The new dataframe has", df_pla.shape[0],
      "rows and", df_pla.shape[1], "columns.")

The new dataframe has 2 rows and 8 columns.

RIVERS

# Selecting data on only Rivers

df_riv = df[df["state"] == "Rivers"].reset_index(drop=True)
df_riv.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	4	5	5	4	Semi Detached Duplex	Port Harcourt	Rivers	85000000.0
1	4	5	5	5	Detached Bungalow	Port Harcourt	Rivers	32000000.0
2	4	4	5	1	Detached Bungalow	Port Harcourt	Rivers	30000000.0

df_riv.shape
print(f"The new dataframe has", df_riv.shape[0],
      "rows and", df_riv.shape[1], "columns.")

The new dataframe has 422 rows and 8 columns.

Having explored each federal state only states with a dataframe of 50+ rows will be considered for data analysis.

States with 50+ rows; Abuja, Anambara, Delta, Edo, Enugu, Imo, Lagos, Ogun, Oyo, and Rivers.

States with less than 50+ rows; Abia, Akwa Ibom, Bayelsa, Borno, Cross River, Ekiti, Kaduna, Kano, Kastina, Kogi, Kwara, Nasarawa, Niger, Ogun and Plateau

ABUJA STATE (Federal Capital Territory)

# Selecting data on only Abuja

df_abj = df[df["state"] == "Abuja"].reset_index(drop=True)
df_abj.head(5)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	6	5	5	4	Detached Duplex	Mabushi	Abuja	450000000.0
1	4	5	5	4	Terraced Duplexes	Katampe	Abuja	800000000.0
2	3	4	5	4	Terraced Duplexes	Lokogoma District	Abuja	25000000.0
3	3	3	4	4	Detached Bungalow	Lokogoma District	Abuja	37000000.0
4	5	6	7	6	Detached Duplex	Katampe	Abuja	160000000.0

df_abj.shape
print(f"The new dataframe has", df_abj.shape[0],
      "rows and", df_abj.shape[1], "columns.")

The new dataframe has 3118 rows and 8 columns.

df_abj.describe()

	bedrooms	bathrooms	toilets	parking_space	price
count	3118.000000	3118.000000	3118.000000	3118.000000	3.118000e+03
mean	4.315266	4.646889	5.108403	4.035600	2.399512e+08
std	1.366342	1.369523	1.406406	1.659792	1.593757e+09
min	1.000000	1.000000	1.000000	1.000000	1.100000e+05
25%	4.000000	4.000000	5.000000	3.000000	4.500000e+07
50%	4.000000	5.000000	5.000000	4.000000	8.500000e+07
75%	5.000000	5.000000	6.000000	4.000000	1.900000e+08
max	9.000000	9.000000	9.000000	9.000000	8.500000e+10

Categorical features

# Summarize the categorical features

df_abj.describe(include=["object"])

	title	town	state
count	3118	3118	3118
unique	7	53	1
top	Detached Duplex	Gwarinpa	Abuja
freq	1283	345	3118

There are

Towns in Abuja

df_abj["town"].unique()

array(['Mabushi', 'Katampe', 'Lokogoma District', 'Kaura', 'Galadimawa',
       'Gwarinpa', 'Lugbe District', 'Jahi', 'Orozo', 'Guzape District',
       'Idu Industrial', 'Utako', 'Kuje', 'Life Camp', 'Dape', 'Gaduwa',
       'Dakwo', 'Asokoro District', 'Wuye', 'Kubwa', 'Apo', 'Wuse 2',
       'Durumi', 'Maitama District', 'Karsana', 'Wuse', 'Kurudu', 'Karmo',
       'Gudu', 'Kukwaba', 'Mbora (Nbora)', 'Jabi', 'Garki', 'Karshi',
       'Kado', 'Nyanya', 'Kyami', 'Dutse', 'Karu', 'Kafe', 'Dakibiyu',
       'Bwari', 'Kagini', 'Mpape', 'Gwagwalada', 'Diplomatic Zones',
       'Kabusa', 'Dei-Dei', 'Duboyi', 'Jikwoyi',
       'Central Business District', 'Wumba', 'Mararaba'], dtype=object)

Property Types

df_abj["title"].unique()

array(['Detached Duplex', 'Terraced Duplexes', 'Detached Bungalow',
       'Semi Detached Duplex', 'Block of Flats', 'Semi Detached Bungalow',
       'Terraced Bungalow'], dtype=object)

Outliers

# Visualise

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_abj["price"], y=df_abj["town"],  palette="Set2",)
sns.stripplot(x=df_abj["price"], y=df_abj["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

InterQuantileRange (IQR)

# Calculating q1 and q3 for price
abj_Q1 = df_abj.price.quantile(0.25)
abj_Q3 = df_abj.price.quantile(0.75)
abj_Q1, abj_Q3

(45000000.0, 190000000.0)

# Calculating InterQuantileRange (IQR) for price
abj_IQR = abj_Q3 - abj_Q1

print("The Interquartile Range for the price is", abj_IQR)

The Interquartile Range for the price is 145000000.0

Lower and Upper Limit

# For price
abj_lower_limit = Q1 - 1.5*abj_IQR
abj_upper_limit = Q3 + 1.5*abj_IQR
abj_lower_limit, abj_upper_limit

(-179500000.0, 377500000.0)

Identifying the Outliers

abj_price_outliers = df_abj[(df_abj.price < abj_lower_limit)
                            | (df_abj.price > abj_upper_limit)]

abj_price_outliers.shape

(413, 8)

Removing the Outliers

# Check the shape before removing the outliers

Abuja = df_abj.shape
Abuja

(3118, 8)

# Remove the outliers using the IQR

df_abj_outlier_free = df_abj[(df_abj.price > abj_lower_limit)
                             & (df_abj.price < abj_upper_limit)]
df_abj_outlier_free.shape

(2705, 8)

# Visualise to validate changes

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_abj_outlier_free["price"],
            y=df_abj_outlier_free["town"],  palette="Set2",)
sns.stripplot(x=df_abj_outlier_free["price"],
              y=df_abj_outlier_free["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

ABUJA REAL ESTATE MARKET TREND (Data Analysis)

Distribution of Features of Properties in Abuja

# Plotting histogram

df_abj_outlier_free.hist(figsize=(16, 16), xrot=90)

plt.show()

Average property price in Abuja

average_price_abj = st.mean(df_abj_outlier_free["price"])

print("The average property price in Abuja is " +
      str(round(average_price_abj, 2)) + " Naira")

The average property price in Abuja is 99302320.7 Naira

Visualise

# Plotting a bar chart for average price per town

average_price_abj = df_abj_outlier_free.groupby(["town"])["price"]\
                    .mean().sort_index()

fig = px.histogram(x=df_abj_outlier_free["town"].
                   value_counts().sort_index().index,
                   y=average_price_abj,
                   color=df_abj_outlier_free["town"].
                   value_counts().sort_index().index)

fig.update_xaxes(title="Town")
fig.update_yaxes(title="Average Property Price per town in Abuja State")
fig.show()

Maitama District, Asokoro District, Wuse and Wuse 2 appear to be the most expensive towns to live in Abuja State.

Most Common Property Type

abj_most_common = st.mode(df_abj_outlier_free["title"])

print("The most common property type in Abuja State is " +
      str(abj_most_common))

The most common property type in Abuja State is Detached Duplex

Anambara

# Selecting data on only Anambara

df_ana = df[df["state"] == "Anambara"].reset_index(drop=True)
df_ana.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	5	5	6	4	Detached Duplex	Lekki	Anambara	260000000.0
1	4	5	5	4	Block of Flats	Ibeju Lekki	Anambara	9000000.0
2	4	4	5	4	Semi Detached Duplex	Lekki	Anambara	75000000.0

df_ana.shape
print(f"The new dataframe has", df_ana.shape[0],
      "rows and", df_ana.shape[1], "columns.")

The new dataframe has 141 rows and 8 columns.

Towns in Anambara

df_ana["town"].unique()

array(['Lekki', 'Ibeju Lekki', 'Agege', 'Ikoyi', 'Mowe Ofada', 'Yaba',
       'Ifako-Ijaiye', 'Mowe Town', 'Port Harcourt', 'Ajah', 'Ikeja',
       'Alimosho', 'Ibadan', 'Ketu', 'Mbora (Nbora)',
       'Victoria Island (VI)', 'Gwarinpa', 'Akure', 'Karu', 'Ikorodu',
       'Ikotun', 'Lokogoma District', 'Shomolu', 'Guzape District',
       'Owerri Municipal', 'Katampe', 'Magboro', 'Gbagada', 'Ojo',
       'Arepo', 'Jabi'], dtype=object)

A quick Google search shows that these towns do not exist in Anambara state. This indicative of wrongly collated data. The data on Anambara state cannot be used for analysis.

DELTA

# Selecting data on only Delta

df_del = df[df["state"] == "Delta"].reset_index(drop=True)
df_del.head(5)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	5	5	6	6	Detached Duplex	Asaba	Delta	100000000.0
1	4	4	5	5	Semi Detached Duplex	Asaba	Delta	65000000.0
2	3	3	4	8	Detached Bungalow	Asaba	Delta	55000000.0
3	4	4	5	5	Detached Duplex	Asaba	Delta	70000000.0
4	5	5	6	5	Detached Duplex	Asaba	Delta	40000000.0

df_del.shape
print(f"The new dataframe has", df_del.shape[0],
      "rows and", df_del.shape[1], "columns.")

The new dataframe has 62 rows and 8 columns.

df_del.describe()

	bedrooms	bathrooms	toilets	parking_space	price
count	62.000000	62.000000	62.000000	62.000000	6.200000e+01
mean	4.129032	4.467742	5.032258	4.112903	1.041210e+08
std	1.273743	1.433987	1.492800	1.660583	2.241452e+08
min	1.000000	2.000000	2.000000	1.000000	4.500000e+06
25%	3.000000	3.000000	5.000000	4.000000	3.100000e+07
50%	4.000000	5.000000	5.000000	4.000000	5.000000e+07
75%	5.000000	5.000000	6.000000	5.000000	9.000000e+07
max	7.000000	9.000000	9.000000	8.000000	1.500000e+09

Categorical features

# Summarize the categorical features

df_del.describe(include=["object"])

	title	town	state
count	62	62	62
unique	7	10	1
top	Detached Duplex	Asaba	Delta
freq	22	50	62

Towns in Delta

df_del["town"].unique()

array(['Asaba', 'Ethiope West', 'Udu', 'Warri', 'Uvwie', 'Aniocha South',
       'Okpe', 'Ughelli South', 'Ughelli North', 'Abraka'], dtype=object)

Property Types

df_del["title"].unique()

array(['Detached Duplex', 'Semi Detached Duplex', 'Detached Bungalow',
       'Block of Flats', 'Terraced Duplexes', 'Terraced Bungalow',
       'Semi Detached Bungalow'], dtype=object)

InterQuantileRange (IQR)

# Plotting a boxplot to visualise the outliers in price column

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_del["price"], y=df_del["town"],  palette="Set2",)
sns.stripplot(x=df_del["price"], y=df_del["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

InterQuantileRange (IQR)

# Calculating q1 and q3 for price
del_Q1 = df_del.price.quantile(0.25)
del_Q3 = df_del.price.quantile(0.75)
del_Q1, del_Q3

(31000000.0, 90000000.0)

# Calculating InterQuantileRange (IQR) for price
del_IQR = del_Q3 - del_Q1

print("The Interquartile Range for the price is", del_IQR)

The Interquartile Range for the price is 59000000.0

# Lower and Upper Limit

del_lower_limit = Q1 - 1.5*del_IQR
del_upper_limit = Q3 + 1.5*del_IQR
del_lower_limit, del_upper_limit

(-50500000.0, 248500000.0)

# Identifying the Outliers

del_price_outliers = df_del[(df_del.price < del_lower_limit)
                            | (df_del.price > del_upper_limit)]

del_price_outliers.shape

(4, 8)

Removing the Outliers

# Check the shape before removing the outliers

Delta = df_del.shape
Delta

(62, 8)

# Remove the outliers using the IQR

df_del_outlier_free = df_del[(df_del.price > del_lower_limit)
                             & (df_del.price < del_upper_limit)]
df_del_outlier_free.shape

(58, 8)

# Plotting a boxplot to validate the changes

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_del_outlier_free["price"],
            y=df_del_outlier_free["town"],  palette="Set2",)
sns.stripplot(x=df_del_outlier_free["price"],
              y=df_del_outlier_free["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

DELTA REAL ESTATE MARKET TREND (Data Analysis)

Distribution of Features of Properties in Delta

# Plotting histogram

df_del_outlier_free.hist(figsize=(16, 16), xrot=90)

plt.show()

Average property price in Delta

del_average_price = st.mean(df_del_outlier_free["price"])

print("The average property price in Delta is " +
      str(round(del_average_price, 2)) + " Naira")

The average property price in Delta is 56991379.31 Naira

# Visualise

# Plotting a bar chart

del_average_price = df_del_outlier_free.groupby(["town"])["price"]\
                    .mean().sort_index()

fig = px.histogram(x=df_del_outlier_free["town"].
                   value_counts().sort_index().index,
                   y=del_average_price,
                   color=df_del_outlier_free["town"].
                   value_counts().sort_index().index)

fig.update_xaxes(title="Town")
fig.update_yaxes(title="Average Property Price per town in Delta State")
fig.show()

Warri and Asaba appear to be the most expensive towns to live in Delta State.

Most Common Property Type

del_most_common = st.mode(df_del_outlier_free["title"])

print("The most common property type in Delta State is " +
      str(del_most_common))

The most common property type in Delta State is Detached Duplex

EDO

# Selecting data on only Edo

df_edo = df[df["state"] == "Edo"].reset_index(drop=True)
df_edo.head(5)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	6	6	6	4	Semi Detached Duplex	Oredo	Edo	45000000.0
1	5	5	6	1	Detached Duplex	Oredo	Edo	70000000.0
2	8	8	8	4	Block of Flats	Oredo	Edo	36000000.0
3	3	2	2	4	Block of Flats	Oredo	Edo	25000000.0
4	4	5	5	4	Block of Flats	Oredo	Edo	35000000.0

df_edo.shape
print(f"The new dataframe has", df_edo.shape[0],
      "rows and", df_edo.shape[1], "columns.")

The new dataframe has 98 rows and 8 columns.

df_edo.describe()

	bedrooms	bathrooms	toilets	parking_space	price
count	98.000000	98.000000	98.000000	98.000000	9.800000e+01
mean	4.224490	4.581633	4.571429	3.642857	5.022449e+07
std	1.665481	1.698535	1.692920	1.561456	4.915944e+07
min	1.000000	1.000000	1.000000	1.000000	3.000000e+06
25%	3.000000	4.000000	4.000000	4.000000	2.500000e+07
50%	4.000000	5.000000	5.000000	4.000000	3.500000e+07
75%	5.000000	5.000000	5.000000	4.000000	5.875000e+07
max	9.000000	9.000000	9.000000	9.000000	2.700000e+08

Categorical features

# Summarize the categorical features

df_edo.describe(include=["object"])

	title	town	state
count	98	98	98
unique	6	5	1
top	Block of Flats	Oredo	Edo
freq	42	90	98

Towns in Edo

df_edo["town"].unique()

array(['Oredo', 'Egor', 'Ovia North-East', 'Uhunmwonde', 'Ikpoba Okha'],
      dtype=object)

Property Types

df_edo["title"].unique()

array(['Semi Detached Duplex', 'Detached Duplex', 'Block of Flats',
       'Detached Bungalow', 'Terraced Duplexes', 'Terraced Bungalow'],
      dtype=object)

Outliers

# Plotting a boxplot to visualise the outliers in price column

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_edo["price"], y=df_edo["town"],  palette="Set2",)
sns.stripplot(x=df_edo["price"], y=df_edo["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

InterQuantileRange (IQR)

# Calculating q1 and q3 for price

edo_Q1 = df_edo.price.quantile(0.25)
edo_Q3 = df_edo.price.quantile(0.75)
edo_Q1, edo_Q3

(25000000.0, 58750000.0)

# Calculating InterQuantileRange (IQR) for price

edo_IQR = edo_Q3 - edo_Q1

print("The Interquartile Range for the price is", edo_IQR)

The Interquartile Range for the price is 33750000.0

# Lower and Upper Limit

edo_lower_limit = Q1 - 1.5*edo_IQR
edo_upper_limit = Q3 + 1.5*edo_IQR
edo_lower_limit, edo_upper_limit

(-12625000.0, 210625000.0)

# Identifying the Outliers
edo_price_outliers = df_edo[(df_edo.price < edo_lower_limit)
                            | (df_edo.price > edo_upper_limit)]

edo_price_outliers.shape

(3, 8)

Removing the Outliers

# Check the shape before removing the outliers

Edo = df_edo.shape
Edo

(98, 8)

# Remove the outliers using the IQR

df_edo_outlier_free = df_edo[(df_edo.price > edo_lower_limit)
                             & (df_edo.price < edo_upper_limit)]
df_edo_outlier_free.shape

(95, 8)

# Plotting a boxplot to validate changes

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_edo_outlier_free["price"],
            y=df_edo_outlier_free["town"],  palette="Set2",)
sns.stripplot(x=df_edo_outlier_free["price"],
              y=df_edo_outlier_free["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

EDO REAL ESTATE MARKET TREND (Data Analysis)

Distribution of Features of Properties in Edo

# Plotting histogram

df_edo_outlier_free.hist(figsize=(16, 16), xrot=90)

plt.show()

Average property price in Edo

edo_average_price = st.mean(df_edo_outlier_free["price"])

print("The average property price in Edo is " +
      str(round(edo_average_price, 2)) + " Naira")

The average property price in Edo is 43547368.42 Naira

# Visualise

# Plotting a bar chart

edo_average_price = df_edo_outlier_free.groupby(["town"])["price"]\
                    .mean().sort_index()

fig = px.histogram(x=df_edo_outlier_free["town"].
                   value_counts().sort_index().index,
                   y=edo_average_price,
                   color=df_edo_outlier_free["town"].
                   value_counts().sort_index().index)

fig.update_xaxes(title="Town")
fig.update_yaxes(title="Average Property Price per town in Edo State")
fig.show()

Oredo appears to be the most expensive town to live in Edo State.

Most Common Property Type

edo_most_common = st.mode(df_edo_outlier_free["title"])

print("The most common property type in Edo State is " +
      str(edo_most_common))

The most common property type in Edo State is Block of Flats

ENUGU

# Selecting data on only Enugu

df_enu = df[df["state"] == "Enugu"].reset_index(drop=True)
df_enu.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	4	4	5	4	Detached Bungalow	Enugu	Enugu	28500000.0
1	4	4	5	4	Detached Duplex	Enugu	Enugu	65000000.0
2	6	5	5	4	Detached Bungalow	Enugu	Enugu	20000000.0

df_enu.shape
print(f"The new dataframe has", df_enu.shape[0],
      "rows and", df_enu.shape[1], "columns.")

The new dataframe has 121 rows and 8 columns.

df_enu.describe()

	bedrooms	bathrooms	toilets	parking_space	price
count	121.000000	121.000000	121.000000	121.000000	1.210000e+02
mean	4.181818	4.462810	4.793388	4.355372	6.667479e+07
std	1.244990	1.477844	1.448662	1.892351	4.600634e+07
min	1.000000	1.000000	1.000000	1.000000	4.500000e+06
25%	4.000000	4.000000	4.000000	4.000000	3.500000e+07
50%	4.000000	4.000000	5.000000	4.000000	5.500000e+07
75%	5.000000	5.000000	5.000000	6.000000	8.000000e+07
max	9.000000	9.000000	8.000000	8.000000	3.200000e+08

# Summarize the categorical features

df_enu.describe(include=["object"])

	title	town	state
count	121	121	121
unique	7	1	1
top	Detached Duplex	Enugu	Enugu
freq	48	121	121

Towns in Enugu

df_enu["town"].unique()

array(['Enugu'], dtype=object)

The dataset contains data on only Enugu town.

Property Types

df_enu["title"].unique()

array(['Detached Bungalow', 'Detached Duplex', 'Block of Flats',
       'Terraced Duplexes', 'Semi Detached Duplex',
       'Semi Detached Bungalow', 'Terraced Bungalow'], dtype=object)

Outliers

# Plotting a boxplot to visualise the outliers in price column

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_enu["price"], y=df_enu["town"],  palette="Set2",)
sns.stripplot(x=df_enu["price"], y=df_enu["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

InterQuantileRange (IQR)

# Calculating q1 and q3 for price
enu_Q1 = df_enu.price.quantile(0.25)
enu_Q3 = df_enu.price.quantile(0.75)
enu_Q1, enu_Q3

(35000000.0, 80000000.0)

# Calculating InterQuantileRange (IQR) for price
enu_IQR = enu_Q3 - enu_Q1

print("The Interquartile Range for the price is", enu_IQR)

The Interquartile Range for the price is 45000000.0

# Lower and Upper Limit

enu_lower_limit = Q1 - 1.5*enu_IQR
enu_upper_limit = Q3 + 1.5*enu_IQR
enu_lower_limit, enu_upper_limit

(-29500000.0, 227500000.0)

# Identifying the Outliers

enu_price_outliers = df_enu[(df_enu.price < enu_lower_limit)
                            | (df_enu.price > enu_upper_limit)]
enu_price_outliers.shape

(2, 8)

Removing the Outliers

# Check the shape before removing the outliers

Enugu = df_enu.shape
Enugu

(121, 8)

# Remove the outliers using the IQR

df_enu_outlier_free = df_enu[(df_enu.price > enu_lower_limit)
                             & (df_enu.price < enu_upper_limit)]
df_enu_outlier_free.shape

(119, 8)

# Plotting a boxplot to validate the changes

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_enu_outlier_free["price"],
            y=df_enu_outlier_free["town"],  palette="Set2",)
sns.stripplot(x=df_enu_outlier_free["price"],
              y=df_enu_outlier_free["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

ENUGU REAL ESTATE MARKET TREND (Data Analysis)

Distribution of Features of Properties in Enugu

# Plotting histogram

df_enu_outlier_free.hist(figsize=(16, 16), xrot=90)

plt.show()

Average property price in Enugu

enu_average_price = st.mean(df_enu_outlier_free["price"])

print("The average property price in Enugu is " +
      str(round(enu_average_price, 2)) + " Naira")

The average property price in Enugu is 63089495.8 Naira

Most Common Property Type

enu_most_common = st.mode(df_enu_outlier_free["title"])

print("The most common property type in Enugu State is " +
      str(enu_most_common))

The most common property type in Enugu State is Detached Duplex

IMO

# Selecting data on only Imo

df_imo = df[df["state"] == "Imo"].reset_index(drop=True)
df_imo.head(5)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	5	3	3	4	Detached Duplex	Owerri North	Imo	50000000.0
1	5	5	6	5	Semi Detached Duplex	Owerri Municipal	Imo	60000000.0
2	4	5	5	4	Block of Flats	Owerri Municipal	Imo	130000000.0
3	4	5	5	4	Block of Flats	Owerri West	Imo	150000000.0
4	2	5	5	4	Block of Flats	Owerri West	Imo	60000000.0

df_imo.shape
print(f"The new dataframe has", df_imo.shape[0],
      "rows and", df_imo.shape[1], "columns.")

The new dataframe has 229 rows and 8 columns.

df_imo.describe()

	bedrooms	bathrooms	toilets	parking_space	price
count	229.000000	229.000000	229.000000	229.000000	2.290000e+02
mean	3.991266	4.135371	4.484716	4.427948	7.414629e+07
std	1.716768	1.740644	1.888558	1.777122	6.048017e+07
min	1.000000	1.000000	1.000000	1.000000	5.850000e+06
25%	3.000000	3.000000	3.000000	4.000000	3.500000e+07
50%	4.000000	5.000000	5.000000	4.000000	5.500000e+07
75%	5.000000	5.000000	6.000000	5.000000	8.500000e+07
max	9.000000	9.000000	9.000000	9.000000	3.000000e+08

# Summarize the categorical features

df_imo.describe(include=["object"])

	title	town	state
count	229	229	229
unique	7	4	1
top	Detached Duplex	Owerri Municipal	Imo
freq	107	194	229

Towns in Imo

df_imo["town"].unique()

array(['Owerri North', 'Owerri Municipal', 'Owerri West', 'Ohaji/Egbema'],
      dtype=object)

Property Types

df_imo["title"].unique()

array(['Detached Duplex', 'Semi Detached Duplex', 'Block of Flats',
       'Detached Bungalow', 'Semi Detached Bungalow', 'Terraced Bungalow',
       'Terraced Duplexes'], dtype=object)

Outliers

# Plotting a boxplot to visualise the outliers in price column

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_imo["price"], y=df_imo["town"],  palette="Set2",)
sns.stripplot(x=df_imo["price"], y=df_imo["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

InterQuantileRange (IQR)

# Calculating q1 and q3 for price

imo_Q1 = df_imo.price.quantile(0.25)
imo_Q3 = df_imo.price.quantile(0.75)
imo_Q1, imo_Q3

(35000000.0, 85000000.0)

# Calculating InterQuantileRange (IQR) for price
imo_IQR = imo_Q3 - imo_Q1

print("The Interquartile Range for the price is", imo_IQR)

The Interquartile Range for the price is 50000000.0

# Lower and Upper Limit

imo_lower_limit = Q1 - 1.5*imo_IQR
imo_upper_limit = Q3 + 1.5*imo_IQR
imo_lower_limit, imo_upper_limit

(-37000000.0, 235000000.0)

# Identifying the Outliers

imo_price_outliers = df_imo[(df_imo.price < imo_lower_limit)
                            | (df_imo.price > imo_upper_limit)]

imo_price_outliers.shape

(9, 8)

Removing the Outliers

# Check the shape before removing the outliers

Imo = df_imo.shape
Imo

(229, 8)

# Remove the outliers using the IQR

df_imo_outlier_free = df_imo[(df_imo.price > imo_lower_limit)
                             & (df_imo.price < imo_upper_limit)]
df_imo_outlier_free.shape

(220, 8)

# Plotting a boxplot to validate the changes

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_imo_outlier_free["price"],
            y=df_imo_outlier_free["town"],  palette="Set2",)
sns.stripplot(x=df_imo_outlier_free["price"],
              y=df_imo_outlier_free["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

IMO REAL ESTATE MARKET TREND (Data Analysis)

Distribution of Features of Properties in Imo

# Plotting histogram

df_imo_outlier_free.hist(figsize=(16, 16), xrot=90)

plt.show()

Average property price in Imo

imo_average_price = st.mean(df_imo_outlier_free["price"])

print("The average property price in Imo is " +
      str(round(imo_average_price, 2)) + " Naira")

The average property price in Imo is 66497727.27 Naira

Visualise

# Plotting a bar chart

imo_average_price = df_imo_outlier_free.groupby(["town"])["price"]\
                    .mean().sort_index()

fig = px.histogram(x=df_imo_outlier_free["town"].
                   value_counts().sort_index().index,
                   y=imo_average_price,
                   color=df_imo_outlier_free["town"].
                   value_counts().sort_index().index)

fig.update_xaxes(title="Town")
fig.update_yaxes(title="Average Property Price per town in Imo State")
fig.show()

Owerri West appears to be the most expensive town to live in Imo State.

Most Common Property Type

imo_most_common = st.mode(df_imo_outlier_free["title"])

print("The most common property type in Imo State is " +
      str(imo_most_common))

The most common property type in Imo State is Detached Duplex

LAGOS STATE

# Selecting data on only Lagos

df_lag = df[df["state"] == "Lagos"].reset_index(drop=True)
df_lag.head(3)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	4	5	5	4	Detached Duplex	Lekki	Lagos	120000000.0
1	4	4	5	6	Detached Duplex	Ajah	Lagos	40000000.0
2	4	4	5	2	Semi Detached Duplex	Lekki	Lagos	75000000.0

df_lag.shape
print(f"The new dataframe has", df_lag.shape[0],
      "rows and", df_lag.shape[1], "columns.")

The new dataframe has 8571 rows and 8 columns.

df_lag.describe()

	bedrooms	bathrooms	toilets	parking_space	price
count	8571.000000	8571.000000	8571.000000	8571.000000	8.571000e+03
mean	4.220978	4.550928	5.075954	4.065453	5.444214e+08
std	1.246955	1.306144	1.374019	1.603682	2.053316e+10
min	1.000000	1.000000	1.000000	1.000000	9.000000e+04
25%	3.000000	4.000000	4.000000	3.000000	4.500000e+07
50%	4.000000	5.000000	5.000000	4.000000	8.200000e+07
75%	5.000000	5.000000	6.000000	5.000000	1.775000e+08
max	9.000000	9.000000	9.000000	9.000000	1.800000e+12

Categorical features

# Summarize the categorical features

df_lag.describe(include=["object"])

	title	town	state
count	8571	8571	8571
unique	7	48	1
top	Detached Duplex	Lekki	Lagos
freq	4430	3392	8571

Towns in Lagos

df_lag["town"].unique()

array(['Lekki', 'Ajah', 'Epe', 'Victoria Island (VI)', 'Ikeja', 'Ikoyi',
       'Magodo', 'Ibeju Lekki', 'Yaba', 'Ifako-Ijaiye', 'Agege',
       'Ikorodu', 'Isheri North', 'Isheri', 'Ipaja', 'Mushin', 'Ejigbo',
       'Isolo', 'Ojodu', 'Alimosho', 'Shomolu', 'Ogudu', 'Surulere',
       'Ayobo', 'Ikotun', 'Maryland', 'Gbagada', 'Idimu', 'Ojo', 'Kosofe',
       'Ilupeju', 'Ketu', 'Ojota', 'Oshodi', 'Amuwo Odofin', 'Ijede',
       'Agbara-Igbesa', 'Ijaiye', 'Apapa', 'Lagos Island', 'Badagry',
       'Oke-Odo', 'Egbe', 'Orile', 'Eko Atlantic City', 'Imota', 'Ijesha',
       'Ibeju'], dtype=object)

Property Types

df_lag["title"].unique()

array(['Detached Duplex', 'Semi Detached Duplex', 'Detached Bungalow',
       'Terraced Duplexes', 'Block of Flats', 'Semi Detached Bungalow',
       'Terraced Bungalow'], dtype=object)

Outliers

# Plotting a boxplot to visualise the outliers in price column

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_lag["price"], y=df_lag["town"],  palette="Set2",)
sns.stripplot(x=df_lag["price"], y=df_lag["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

The box plot above show majority of the dataoiints on the left side, however, there some datapoints on the that appear separated from the other datapoints as you begin to scan towards the right part of the chart. These point indicate the presence of Outliers. These outliers are properties with significantly higher prices when compared to the property prices within Lagos State. The "lekki" and "Ikoyi" appear to have more of these outliers.

InterQuantileRange (IQR)

# Calculating q1 and q3 for price
lag_Q1 = df_lag.price.quantile(0.25)
lag_Q3 = df_lag.price.quantile(0.75)
lag_Q1, lag_Q3

(45000000.0, 177500000.0)

# Calculating InterQuantileRange (IQR) for price
lag_IQR = lag_Q3 - lag_Q1

print("The Interquartile Range for the price is", lag_IQR)

The Interquartile Range for the price is 132500000.0

# Lower and Upper Limit

lag_lower_limit = Q1 - 1.5*lag_IQR
lag_upper_limit = Q3 + 1.5*lag_IQR
lag_lower_limit, lag_upper_limit

(-160750000.0, 358750000.0)

Identifying the Outliers

lag_price_outliers = df_lag[(df_lag.price < lag_lower_limit)
                            | (df_lag.price > lag_upper_limit)]

lag_price_outliers.shape

(917, 8)

Removing the Outliers

Check the shape before removing the outliers

Lagos = df_lag.shape
Lagos

(8571, 8)

Remove the outliers using the IQR

df_lag_outlier_free = df_lag[(df_lag.price > lag_lower_limit)
                             & (df_lag.price < lag_upper_limit)]
df_lag_outlier_free.shape

(7654, 8)

Visualise to validate changes

# Plotting a boxplot to validate changes

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_lag_outlier_free["price"],
            y=df_lag_outlier_free["town"],  palette="Set2",)
sns.stripplot(x=df_lag_outlier_free["price"],
              y=df_lag_outlier_free["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

Re-plotting the box plot indicates the absence of the outliers initially spotted. However, this chart reveals that certain towns such as "IKoyi", "Lekki", "Victoria Island", "Magodo", "Apapa", "Ikeja" and "Lagos Island" have properties with significantly higher prices than other towns within Lagos.

LAGOS REAL ESTATE MARKET TREND (Data Analysis)

Distribution of Features of Properties in Lagos

# Plotting histogram

df_lag_outlier_free.hist(figsize=(16, 16), xrot=90)

plt.show()

Average property price in Lagos

average_price_lag = st.mean(df_lag_outlier_free["price"])

print("The average property price in Lagos is " +
      str(round(average_price_lag, 2)) + " Naira")

The average property price in Lagos is 100034223.99 Naira

Visualise

# Plotting a bar chart of average price per town

average_price_lag = df_lag_outlier_free.groupby(["town"])["price"]\
                    .mean().sort_index()

fig = px.histogram(x=df_lag_outlier_free["town"].
                   value_counts().sort_index().index,
                   y=average_price_lag,
                   color=df_lag_outlier_free["town"].
                   value_counts().sort_index().index)

fig.update_xaxes(title="Town")
fig.update_yaxes(title="Average Property Price per town in Lagos State")
fig.show()

Ikoyi, Victoria Island (VI) and Lekki appear to be the most expensive towns to live in Lagos State.

Most Common Property Type

lag_most_common = st.mode(df_lag_outlier_free["title"])

print("The most common property type in Lagos State is " +
      str(lag_most_common))

The most common property type in Lagos State is Detached Duplex

Write a conclusion

OGUN

# Selecting data on only Ogun

df_ogu = df[df["state"] == "Ogun"].reset_index(drop=True)
df_ogu.head(5)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	3	4	4	3	Detached Bungalow	Mowe Ofada	Ogun	12000000.0
1	2	3	5	4	Block of Flats	Mowe Ofada	Ogun	7000000.0
2	2	2	2	4	Detached Bungalow	Abeokuta North	Ogun	4000000.0
3	1	2	2	1	Block of Flats	Sango Ota	Ogun	6900000.0
4	4	2	2	1	Terraced Bungalow	Sango Ota	Ogun	5000000.0

df_ogu.shape
print(f"The new dataframe has", df_ogu.shape[0],
      "rows and", df_ogu.shape[1], "columns.")

The new dataframe has 579 rows and 8 columns.

df_ogu.describe()

	bedrooms	bathrooms	toilets	parking_space	price
count	579.000000	579.000000	579.000000	579.000000	5.790000e+02
mean	3.345423	3.758204	4.108808	3.803109	2.411517e+07
std	1.297399	1.341479	1.387129	1.528028	2.289475e+07
min	1.000000	1.000000	1.000000	1.000000	1.000000e+05
25%	3.000000	3.000000	3.000000	3.000000	1.000000e+07
50%	3.000000	4.000000	4.000000	4.000000	1.600000e+07
75%	4.000000	5.000000	5.000000	4.000000	3.000000e+07
max	9.000000	9.000000	9.000000	9.000000	1.550000e+08

Categorical features

# Summarize the categorical features

df_ogu.describe(include=["object"])

	title	town	state
count	579	579	579
unique	7	22	1
top	Detached Bungalow	Mowe Ofada	Ogun
freq	225	137	579

Towns in Ogun State

df_ogu["town"].unique()

array(['Mowe Ofada', 'Abeokuta North', 'Sango Ota', 'Ifo', 'Ado-Odo/Ota',
       'Isheri North', 'Sagamu', 'Ibafo', 'Mowe Town', 'Simawa', 'Arepo',
       'Abeokuta South', 'KM 46', 'Magboro', 'Obafemi Owode', 'Ogijo',
       'Ijoko', 'Oke-Aro', 'Yewa South', 'Agbara', 'Ijebu Ode', 'Ewekoro'],
      dtype=object)

Property Types

df_ogu["title"].unique()

array(['Detached Bungalow', 'Block of Flats', 'Terraced Bungalow',
       'Detached Duplex', 'Terraced Duplexes', 'Semi Detached Bungalow',
       'Semi Detached Duplex'], dtype=object)

Outliers

# Plotting a boxplot to visualise the outliers in price column

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_ogu["price"], y=df_ogu["town"],  palette="Set2",)
sns.stripplot(x=df_ogu["price"], y=df_ogu["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

InterQuantileRange (IQR)

# Calculating q1 and q3 for price
ogu_Q1 = df_ogu.price.quantile(0.25)
ogu_Q3 = df_ogu.price.quantile(0.75)
ogu_Q1, ogu_Q3

(10000000.0, 30000000.0)

# Calculating InterQuantileRange (IQR) for price

ogu_IQR = ogu_Q3 - ogu_Q1

print("The Interquartile Range for the price is", ogu_IQR)

The Interquartile Range for the price is 20000000.0

# Lower and Upper Limit

ogu_lower_limit = Q1 - 1.5*ogu_IQR
ogu_upper_limit = Q3 + 1.5*ogu_IQR
ogu_lower_limit, ogu_upper_limit

(8000000.0, 190000000.0)

# Identifying the Outliers

ogu_price_outliers = df_ogu[(df_ogu.price < ogu_lower_limit)
                            | (df_ogu.price > ogu_upper_limit)]

ogu_price_outliers.shape

(82, 8)

Removing the Outliers

# Check the shape before removing the outliers

Ogun = df_ogu.shape
Ogun

(579, 8)

# Remove the outliers using the IQR

df_ogu_outlier_free = df_ogu[(df_ogu.price > ogu_lower_limit)
                             & (df_ogu.price < ogu_upper_limit)]
df_ogu_outlier_free.shape

(484, 8)

# Plotting a boxplot to validate changes

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_ogu_outlier_free["price"],
            y=df_ogu_outlier_free["town"],  palette="Set2",)
sns.stripplot(x=df_ogu_outlier_free["price"],
              y=df_ogu_outlier_free["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

OGUN REAL ESTATE MARKET TREND (Data Analysis)

Distribution of Features of Properties in Ogun

# Plotting histogram

df_ogu_outlier_free.hist(figsize=(16, 16), xrot=90)

plt.show()

Average property price in Ogun

ogu_average_price = st.mean(df_ogu_outlier_free["price"])

print("The average property price in Ogun is " +
      str(round(ogu_average_price, 2)) + " Naira")

The average property price in Ogun is 27662570.25 Naira

# Visualise

# Plotting a bar chart

ogu_average_price = df_ogu_outlier_free.groupby(["town"])["price"]\
                    .mean().sort_index()

fig = px.histogram(x=df_ogu_outlier_free["town"].
                   value_counts().sort_index().index,
                   y=ogu_average_price,
                   color=df_ogu_outlier_free["town"].
                   value_counts().sort_index().index)

fig.update_xaxes(title="Town")
fig.update_yaxes(title="Average Property Price per town in Ogun State")
fig.show()

Ewekoro, Ijoko, and Isheri North appear to be the most expensive towns to live in Ogun State.

Most Common Property Type

ogu_most_common = st.mode(df_ogu_outlier_free["title"])

print("The most common property type in Ogun State is " +
      str(ogu_most_common))

The most common property type in Ogun State is Detached Bungalow

OYO STATE

# Selecting data on only Oyo

df_oyo = df[df["state"] == "Oyo"].reset_index(drop=True)
df_oyo.head(5)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	3	3	3	4	Detached Bungalow	Ibadan	Oyo	10000000.0
1	8	1	1	4	Detached Duplex	Ibadan	Oyo	150000000.0
2	3	2	2	4	Detached Bungalow	Ibadan	Oyo	11000000.0
3	3	3	3	4	Detached Bungalow	Ibadan	Oyo	18000000.0
4	2	2	3	4	Detached Bungalow	Egbeda	Oyo	3000000.0

df_oyo.shape
print(f"The new dataframe has", df_oyo.shape[0],
      "rows and", df_oyo.shape[1], "columns.")

The new dataframe has 435 rows and 8 columns.

df_oyo.describe()

	bedrooms	bathrooms	toilets	parking_space	price
count	435.000000	435.000000	435.000000	435.000000	4.350000e+02
mean	3.951724	4.190805	4.381609	4.156322	4.383931e+07
std	1.470122	1.591425	1.646567	1.336625	4.419087e+07
min	1.000000	1.000000	1.000000	1.000000	1.400000e+06
25%	3.000000	3.000000	3.000000	4.000000	1.500000e+07
50%	4.000000	4.000000	5.000000	4.000000	3.100000e+07
75%	5.000000	5.000000	5.000000	4.000000	6.000000e+07
max	9.000000	9.000000	9.000000	8.000000	3.500000e+08

Categorical features

# Summarize the categorical features

df_oyo.describe(include=["object"])

	title	town	state
count	435	435	435
unique	7	12	1
top	Detached Bungalow	Ibadan	Oyo
freq	151	370	435

Towns in Oyo

df_oyo["town"].unique()

array(['Ibadan', 'Egbeda', 'Ibadan South-West', 'Oluyole', 'Ido',
       'Afijio', 'Ibadan North', 'Ibarapa North', 'Akinyele',
       'Ibadan North-East', 'Oyo West', 'Ibadan North-West'], dtype=object)

Property Types

df_oyo["title"].unique()

array(['Detached Bungalow', 'Detached Duplex', 'Block of Flats',
       'Semi Detached Duplex', 'Terraced Bungalow', 'Terraced Duplexes',
       'Semi Detached Bungalow'], dtype=object)

Outliers

# Plotting a boxplot to visualise the outliers in price column

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_oyo["price"], y=df_oyo["town"],  palette="Set2",)
sns.stripplot(x=df_oyo["price"], y=df_oyo["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

InterQuantileRange (IQR)

# Calculating q1 and q3 for price
oyo_Q1 = df_oyo.price.quantile(0.25)
oyo_Q3 = df_oyo.price.quantile(0.75)
oyo_Q1, oyo_Q3

(15000000.0, 60000000.0)

# Calculating InterQuantileRange (IQR) for price

oyo_IQR = oyo_Q3 - oyo_Q1

print("The Interquartile Range for the price is", oyo_IQR)

The Interquartile Range for the price is 45000000.0

# Lower and Upper Limit

oyo_lower_limit = Q1 - 1.5*oyo_IQR
oyo_upper_limit = Q3 + 1.5*oyo_IQR
oyo_lower_limit, oyo_upper_limit

(-29500000.0, 227500000.0)

# Identifying the Outliers

oyo_price_outliers = df_oyo[(df_oyo.price < oyo_lower_limit)
                            | (df_oyo.price > oyo_upper_limit)]

oyo_price_outliers.shape

(5, 8)

Removing the Outliers

# Check the shape before removing the outliers
Oyo = df_oyo.shape
Oyo

(435, 8)

# Remove the outliers using the IQR

df_oyo_outlier_free = df_oyo[(df_oyo.price > oyo_lower_limit)
                             & (df_oyo.price < oyo_upper_limit)]
df_oyo_outlier_free.shape

(430, 8)

# Plotting a boxplot to validate the changes

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_oyo_outlier_free["price"],
            y=df_oyo_outlier_free["town"],  palette="Set2",)
sns.stripplot(x=df_oyo_outlier_free["price"],
              y=df_oyo_outlier_free["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

OYO RESIDENTIAL REAL ESTATE MARKET TREND (Data Analysis)

Distribution of Features of Properties in Oyo

# Plotting histogram

df_oyo_outlier_free.hist(figsize=(16, 16), xrot=90)

plt.show()

Comment here

Average property price in Oyo

oyo_average_price = st.mean(df_oyo_outlier_free["price"])

print("The average property price in Oyo is " +
      str(round(oyo_average_price, 2)) + " Naira")

The average property price in Oyo is 41093255.81 Naira

# Visualise

# Plotting a bar chart

oyo_average_price = df_oyo_outlier_free.groupby(["town"])["price"]\
                    .mean().sort_index()

fig = px.histogram(x=df_oyo_outlier_free["town"].
                   value_counts().sort_index().index,
                   y=oyo_average_price,
                   color=df_oyo_outlier_free["town"].
                   value_counts().sort_index().index)

fig.update_xaxes(title="Town")
fig.update_yaxes(title="Average Property Price per town in Oyo State")
fig.show()

Ibadan South-West, Ibadan and Oluyole appear to be the most expensive towns to live in Oyo State.

Most Common Property Type

oyo_most_common = st.mode(df_oyo_outlier_free["title"])

print("The most common property type in Oyo State is " +
      str(oyo_most_common))

The most common property type in Oyo State is Detached Bungalow

RIVERS STATE

# Selecting data on only Rivers

df_riv = df[df["state"] == "Rivers"].reset_index(drop=True)
df_riv.head(5)

	bedrooms	bathrooms	toilets	parking_space	title	town	state	price
0	4	5	5	4	Semi Detached Duplex	Port Harcourt	Rivers	85000000.0
1	4	5	5	5	Detached Bungalow	Port Harcourt	Rivers	32000000.0
2	4	4	5	1	Detached Bungalow	Port Harcourt	Rivers	30000000.0
3	3	5	5	4	Block of Flats	Obio-Akpor	Rivers	100000000.0
4	5	5	6	5	Detached Duplex	Port Harcourt	Rivers	180000000.0

df_riv.shape
print(f"The new dataframe has", df_riv.shape[0],
      "rows and", df_riv.shape[1], "columns.")

The new dataframe has 422 rows and 8 columns.

df_riv.describe()

	bedrooms	bathrooms	toilets	parking_space	price
count	422.000000	422.000000	422.000000	422.000000	4.220000e+02
mean	4.350711	4.713270	5.206161	4.739336	1.327626e+08
std	1.302377	1.338544	1.299611	2.268930	4.079943e+08
min	1.000000	1.000000	1.000000	1.000000	6.000000e+06
25%	4.000000	4.000000	5.000000	4.000000	3.586043e+07
50%	4.000000	5.000000	5.000000	4.000000	6.000000e+07
75%	5.000000	5.000000	6.000000	6.000000	1.000000e+08
max	9.000000	9.000000	9.000000	9.000000	4.000000e+09

Categorical features

# Summarize the categorical features

df_riv.describe(include=["object"])

	title	town	state
count	422	422	422
unique	7	5	1
top	Detached Duplex	Port Harcourt	Rivers
freq	233	410	422

Towns in Rivers

df_riv["town"].unique()

array(['Port Harcourt', 'Obio-Akpor', 'Oyigbo', 'Eleme', 'Ikwerre'],
      dtype=object)

Property Types

df_riv["title"].unique()

array(['Semi Detached Duplex', 'Detached Bungalow', 'Block of Flats',
       'Detached Duplex', 'Terraced Bungalow', 'Semi Detached Bungalow',
       'Terraced Duplexes'], dtype=object)

Outliers

# Plotting a boxplot to visualise the outliers in price column

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_riv["price"], y=df_riv["town"],  palette="Set2",)
sns.stripplot(x=df_riv["price"], y=df_riv["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

InterQuantileRange (IQR)

# Calculating q1 and q3 for price

riv_Q1 = df_riv.price.quantile(0.25)
riv_Q3 = df_riv.price.quantile(0.75)
riv_Q1, riv_Q3

(35860434.0, 100000000.0)

# Calculating InterQuantileRange (IQR) for price

riv_IQR = riv_Q3 - riv_Q1

print("The Interquartile Range for the price is", riv_IQR)

The Interquartile Range for the price is 64139566.0

# Lower and Upper Limit

riv_lower_limit = Q1 - 1.5*riv_IQR
riv_upper_limit = Q3 + 1.5*riv_IQR
riv_lower_limit, riv_upper_limit

(-58209349.0, 256209349.0)

# Identifying the Outliers

riv_price_outliers = df_riv[(df_riv.price < riv_lower_limit)
                            | (df_riv.price > riv_upper_limit)]

riv_price_outliers.shape

(16, 8)

Removing the Outliers

# Check the shape before removing the outliers

Rivers = df_riv.shape
Rivers

(422, 8)

# Remove the outliers using the IQR

df_riv_outlier_free = df_riv[(df_riv.price > riv_lower_limit)
                             & (df_riv.price < riv_upper_limit)]
df_riv_outlier_free.shape

(406, 8)

# Plotting a boxplot to validate the changes

# Styling the plot
sns.set_theme(style="whitegrid")

# Scaling the plot
fig_dims = (20, 20)
fig, ax = plt.subplots(figsize=fig_dims)

# Plotting
sns.boxplot(x=df_riv_outlier_free["price"],
            y=df_riv_outlier_free["town"],  palette="Set2",)
sns.stripplot(x=df_riv_outlier_free["price"],
              y=df_riv_outlier_free["town"], color=".25",
              jitter=True,
              marker='o',
              alpha=0.8)

<AxesSubplot:xlabel='price', ylabel='town'>

RIVERS STATE REAL ESTATE MARKET TREND (Data Analysis)

Distribution of Features of Properties in Rivers

# Plotting histogram

df_riv_outlier_free.hist(figsize=(16, 16), xrot=90)

plt.show()

Average property price in Rivers

riv_average_price = st.mean(df_riv_outlier_free["price"])

print("The average property price in Rivers State is " +
      str(round(riv_average_price, 2)) + " Naira")

The average property price in Rivers State is 73031561.36 Naira

# Visualise

# Plotting a bar chart

riv_average_price = df_riv_outlier_free.groupby(["town"])["price"]\
                    .mean().sort_index()

fig = px.histogram(x=df_riv_outlier_free["town"].
                   value_counts().sort_index().index,
                   y=riv_average_price,
                   color=df_riv_outlier_free["town"].
                   value_counts().sort_index().index)

fig.update_xaxes(title="Town")
fig.update_yaxes(title="Average Property Price per town in Rivers State")
fig.show()

Port Harcourt appears to be the most expensive town to live in Rivers State.

Most Common Property Type

riv_most_common = st.mode(df_riv_outlier_free["title"])

print("The most common property type in Rivers State is " +
      str(riv_most_common))

The most common property type in Rivers State is Detached Duplex

CONCLUSIONS AND RECOMMENDATIONS

The following conclusions were drawn based on the findings;

Usually in real estate, the price is dependent on certain factors such as location, size, furnishing and features of the property. This seemed to be the case with the Nigerian real estate market.

Borno appeared to have the highest property prices which is rather unusual. This is because over the past decade, Borno state as well as it neighbouring states have been severely affected by series of attacks by Boko Haram. This would most likely lead to a decrease in property value or a stall in property price increase. This finding questions the quality of the dataset used for this project and the property market in Borno State.

As the project progressed, it became clear that the columns labelled "title" and "bedrooms" were misleading in some entries. For instance, a survey of the website where the dataset was scraped from revealed that it is uncommon for a block of flats in Nigeria to have only 4 bedrooms. Rather, a block of flats would have 4 or more flats, with each flat having 1 to 4 bedrooms. For clarity, a block of flats with 4 flats and 2 bedrooms each would have 8 bedrooms in total. Therefore an entry in the dataset with 4 bedrooms and titled "block of flats" could either mean a block of flats with 4 one-bedroom flats or a specific 4-bedrooms flat in a block of flats. This leads to inaccurate analysis, as the "price" could be the value of the entire block of flats or one flat in a block of flats.

Next, a quick Google search showed that the towns tagged as "Anambara" in the "state" column do not exist in Anambara State. The towns appear to be in Lagos, Abuja and Ondo state respectively. This is indicative of wrongly collated data. This further reduced the quality of the dataset.

In addition, asides from features like number of bedrooms, toilets, type of property, town and state; there are not much information available on the properties. This does not provide sufficient information on the property and could make price prediction a difficult task.

Conclusively, to get more accurate insights on the Nigerian property market, care needs to be taken when scrapping the data from property websites and attention to details is necessary when labelling the data in a table format.

References

- Nigeria: https://en.wikipedia.org/wiki/Nigeria

- Anambara: https://en.wikipedia.org/wiki/Anambra_State#Cities_and_administrative_divisions